{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.",
    "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.",
    "baseline": "The same model at its original precision, on the same GPU.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Peak memory, task pass rate, latency, and total serving cost at the same context and load.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.11226",
  "title": "When Lower Reconstruction Loss Hurts: Distributionally Robust Refinement for Low-Bit LLM Quantization",
  "summary": "DRQ replaces the assumption that lower reconstruction loss during weight-only PTQ yields better quantized models; it adds a post-hoc refinement step that re-optimizes integer codes within the existing quantization grid to minimize worst-case loss over a set of activation distributions, leaving quantization parameters and inference kernels unchanged. The cost is extra offline computation at quantization time plus implementation complexity, with zero added inference latency or memory. It can fail if the constrained distribution set does not cover the actual deployment activation distribution, in which case the robustness guarantee does not transfer and gains may vanish on the target workload.",
  "rationale": "The method is a plausible drop-in improvement to PTQ pipelines the reader already runs on a single GPU, but its benefit depends on the match between the assumed activation distribution set and the reader's actual workload, so it requires task-specific validation before adoption.",
  "technique": "Distributionally Robust Quantization refinement (DRQ)",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11226",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "Improves downstream performance of models quantized by six PTQ methods (including AWQ and GPTQ) across dense and mixture-of-experts LLMs, with no added inference overhead; no multiplicative factor is reported in the abstract.",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    {
      "dimension": "reproducibility",
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  ],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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}